A Hybrid Approach to Tiger Re-Identification

Visual data analytics is increasingly becoming an important part of wildlife monitoring and conservation strategies. In this work, we discuss our solution to the image-based Amur tiger re-identification (Re-ID) challenge hosted by the CVWC Workshop at ICCV 2019. Various factors like poor quality images, lighting and pose variations, and limited images per identity make tiger Re-ID a difficult task for deep learning models. Consequently, we propose to utilize both deep learning and traditional SIFT descriptor-based matching for tiger re-identification. The proposed deep network is based on a DenseNet model, fine-tuned by minimizing a classification cross-entropy loss regularized by a pairwise KL-divergence loss that promotes better semantically discriminative features. We also utilize several data transformations to improve the model's robustness and generalization across views and image quality variations. We establish the efficacy of our approach on the 'Plain Re-ID' challenge task by reporting results on the pre-cropped tiger Re-ID dataset. To further test our Re-ID model's robustness to detection quality, we also report results on the 'Wild Re-ID' task, which incorporates learning a tiger detection model. We show that our model is able to perform well on both the plain and wild Re-ID tasks. Code will be available at https://github.com/FGVC/DelPro.